Evidence map›Paper›PMID 33963364›Full record

ArticleHealth policy and planning2021

Who will drop out of voluntary social health insurance? Evidence from the New Cooperative Medical Scheme in China.

Mingming Xu, Wei Yang

Open access · hybridAbstract read
In one paragraph

Article in Health policy and planning, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.6field-weighted citation impact, top 15% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 9 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 2 institutions in 2 countries.

Mingming XuDepartment of Economics and Management, Karlsruhe Institute of Technology, Karlsruhe, Kronenstraße 34, 76133 Karlsruhe, Germany.ORCID 0000-0002-0160-2981
Wei YangDepartment of Global Health and Social Medicine, Faculty of Social Science and Public Policy, King's College London, 3.09 Bush House NE, Strand, London WC2R 2LS, UK.ORCID 0000-0001-7249-4386
Karlsruhe Institute of Technology · DEKing's College London · GB

Funding

China Scholarship Council (CSC)
6 · The paper itself

Abstract

Although public voluntary health insurance (VHI) has been adopted in many low- and middle-income countries to improve access to care for the population, a common issue with VHI is its high dropout rate. Using the New Cooperative Medical Scheme (NCMS)-a public VHI in China-as a case study, this article employs a fixed-effects negative binomial regression model combining the difference-in-difference-in-differences approach and multivariate distance matching to examine the factors associated with dropping out and the impact of dropout on outpatient care utilization among middle-aged and older people in rural China. Drawing data from the China Health and Retirement Longitudinal Study, our results showed that healthy people, vulnerable people and people who use less healthcare tended to drop out. Dropout had a significant negative impact on outpatient care utilization, especially for those with worse health statuses and those living in poorer provinces. We also found that the impact of dropout on outpatient utilization was more pronounced at secondary and tertiary hospitals than at primary care clinics. We urge policymakers to rethink the design of the NCMS by waiving premiums for the most vulnerable people.

Indexed as

Insurance, HealthRural PopulationAgedChinaDelivery of Health CareHealth ExpendituresHumansLongitudinal StudiesMiddle AgedChinadrop outPublic voluntary health insurancethe New Cooperative Medical Scheme

Identifiers

PMID33963364
PMCPMC8530158
OpenAlexW3159940885

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.